From MilkingBots to RoboDolphins: How AI changes human-animal relations and enables alienation towards animals

The paper addresses a gap in technology ethics by focusing on how AI and robotics reshape human-animal relations, an area often overlooked due to an anthropocentric focus in existing literature. The authors aim to answer how AI affects human-animal relations and what the ethical implications are. They focus on two dominant processes through which AI exerts influence: automation (especially in animal agriculture/precision livestock farming) and replacement (substituting biological animals with AI-driven robotic animals in zoos, as companions, and in laboratories). The purpose is both descriptive—mapping changes at individual and societal levels—and normative—evaluating the moral significance of these changes from perspectives that acknowledge the moral importance of human-animal relations. The study argues that shifts in relations matter ethically and that AI’s role in catalyzing alienation or, alternatively, enabling more respectful relations warrants careful scrutiny to guide responsible AI development and use.

The paper situates its analysis within three main ethical approaches that recognize the moral significance of human-animal relations: (1) Relational accounts rejecting property-based moral status (Diamond 1978; Crary 2010), which locate moral importance in existing moral relations rather than in shared capacities; (2) Relational critiques of status ascription that emphasize relations over properties and highlight the power dynamics in conferring moral status (Coeckelbergh & Gunkel 2014); and (3) Context-sensitive property-based views (e.g., sentientism) that assign special duties arising from specific relationships (Palmer 2010), including cooperation-based arguments (Coeckelbergh 2009). The authors also reference work on precision livestock farming’s ethical implications (Bos et al. 2018; Tuyttens et al. 2022) and cautionary views about AI in farming (Birch 2023; Simoneau-Gilbert & Birch 2024). They note how language and AI language models can perpetuate speciesist biases (Hagendorff et al. 2022), while LLMs could be designed to counter such biases (Ghose et al. 2024). Empirical human–animal relation studies (Adams 2018; Wells 2019; Raut et al. 2020) and evidence on animal emotions and sociality (Bekoff 2003; Whitehead 1997) inform the moral stakes. The literature also covers zoomorphic social robots in therapy/care (Paro; Pu et al. 2019; Gustafsson et al. 2015; Moyle et al. 2015, 2016; Heylen et al. 2012; Sharkey & Wood 2014) and replacement of animals in labs via AI-driven alternatives (Hartung 2016; Luechtefeld et al. 2018).

This is a conceptual and normative analysis rather than an empirical study. The authors: (1) delineate two predominant AI-mediated processes affecting human-animal relations—automation (primarily in animal agriculture/PLF) and replacement (via zoomorphic social robots in zoos and companionship, and AI-enabled alternatives in laboratories); (2) analyze illustrative cases (e.g., automated milking and surveillance; AI disease detection and facial recognition; RoboDolphins; robotic companion animals like Paro, JustoCat, Aibo; drones walking dogs; lab testing alternatives) to describe how relational qualities change at both interpersonal and societal levels; and (3) evaluate these changes ethically through relational and context-sensitive frameworks, emphasizing the notion of alienation and the role of language and LLMs in shaping societal perspectives. No datasets were generated or analyzed; claims are supported by philosophical argument, prior literature, and case exemplars.

• AI changes human–animal relations mainly via two processes: automation and replacement.
• Automation in animal agriculture (PLF) increases distance and alienation between humans and farmed animals by replacing human caregivers with machines across monitoring, diagnosis, feeding, milking, and handling, risking erosion of remaining care-based interactions. This can obscure practices, lower transparency, and harden consumer detachment from animals used for food; it may hinder transitions toward more respectful agriculture. Despite welfare-oriented rhetoric, removing human expertise can reduce sensitivity to individual animal needs and may harm welfare.
• Language contributes to alienation, and AI language models currently mirror speciesist societal biases (e.g., differential moral consideration for dogs vs. pigs). However, LLMs could be designed to counter such biases and educate toward less instrumental views of animals.
• Replacement via zoomorphic robots has heterogeneous effects: • Zoos/entertainment: Replacing captive animals (e.g., RoboDolphins) can avoid captivity-related harms and does not forfeit valuable reciprocity, since captivity interactions are already non-reciprocal and instrumental. Replacement may thus overall benefit animals and improve human–animal relations by reducing direct instrumentalization. • Companion animals: Robotic companions may reduce animal abuse and provide some benefits, but risk perpetuating problematic expectations of permanent availability and reinforcing instrumental perspectives. The moral impact depends on people’s ability to distinguish artificial from biological animals and to maintain respectful attitudes toward real animals. • Laboratories/food alternatives: AI-enabled methods can reduce or replace animal testing (e.g., ML "read-across", organoids) and support cultured products, potentially diminishing instrumentalization and burdensome human–animal interactions.
• The concept of alienation is central: AI can facilitate alienation (especially via automation and language), but could also be used to counter alienation (e.g., bias-aware LLMs, technologies designed to foster respect).

The analysis answers how AI affects human–animal relations by showing that AI restructures relational dynamics both individually (farmer–animal, companion–animal, zoo visitor–animal) and societally (consumer perceptions, linguistic framing). Ethically, automation commonly undermines care relations and deepens alienation, risking diminished welfare attention and reduced societal visibility of animals’ lives. Replacement is more nuanced: substituting biological zoo animals with robots can mitigate captivity harms without sacrificing morally valuable reciprocity; in companionship, benefits such as reduced abuse are counterbalanced by risks of normalizing availability and instrumental attitudes unless users clearly differentiate robots from animals and cultivate respect. The paper highlights language and LLMs as powerful mediators that can entrench speciesist biases or be reoriented to challenge them. It also raises the “chicken-and-egg” dynamic: some AI replacements may reflect prior shifts in societal valuation of animals (e.g., public concern driving RoboDolphins), while automation largely perfects existing exploitative systems rather than responds to a moral shift. The authors advocate a precautionary stance in moral status discourse, given dynamic, historically contingent relations and limited knowledge of nonhuman lives. They propose that advanced technologies could be designed to foster less alienated, more respectful relations if guided by interdisciplinary, non-anthropocentric ethics.

AI and robotics reshape human–animal relations chiefly through automation and replacement. Automation in agriculture tends to exacerbate alienation, eroding care-based relations and potentially hindering more respectful practices. Replacement can, in some domains, benefit animals and human–animal relations—especially replacing captive zoo animals with robotic counterparts—though it raises its own ethical issues. Alienation emerges as a unifying theme, driven by technological mediation and language, yet not an inevitable outcome: with deliberate design and ethical orientation, technologies (including LLMs) could help cultivate more respectful, non-instrumental relations. The authors call for further interdisciplinary research connecting technical development with (post-)humanities, careful study of relational shifts in each context, and a precautionary approach to protecting animals amidst evolving relations.

• The discussion is selective and illustrative rather than exhaustive; it focuses on prominent cases in agriculture, zoos/entertainment, companionship, and laboratories.
• The paper is conceptual and normative; it does not provide new empirical data and calls for further studies (e.g., on welfare impacts of PLF, human well-being effects of robotic companions in typical home settings, and public perception shifts).
• Many AI applications referenced are emerging or in pilot phases; conclusions about long-term relational and welfare impacts involve uncertainty.
• The analysis brackets a full treatment of human–robot ethics and the complexities of zoo ethics, noting these as areas for future work.